What GAO Found
GAO found that diagnostic coding differences exist between MA plans and Medicare FFS. Using data on beneficiary characteristics and regression analysis, GAO estimated that before CMSs adjustment, 2010 MA beneficiary risk scores were at least 4.8 percent, and perhaps as much as 7.1 percent, higher than they likely would have been if the same beneficiaries had been continuously enrolled in FFS. The higher risk scores were equivalent to $3.9 billion to $5.8 billion in payments to MA plans. Both GAO and CMS found that the impact of coding differences increased over time. This trend suggests that the cumulative impact of coding differences in 2011 and 2012 could be larger than in 2010.
In contrast to GAO, CMS estimated that 3.4 percent of 2010 MA beneficiary risk scores were attributable to coding differences between MA plans and Medicare FFS. CMSs adjustment for this difference avoided $2.7 billion in excess payments to MA plans. CMSs 2010 estimate differs from GAOs in that CMSs methodology did not include more current data, did not incorporate the trend of the impact of coding differences over time, and did not account for beneficiary characteristics other than age and mortality, such as sex, health status, Medicaid enrollment status, beneficiary residential location, and whether the original reason for Medicare entitlement was disability.
CMS did not update its coding adjustment estimate in 2011 and 2012 to include more current data, to account for additional years of coding differences, or to incorporate the trend of the impact of coding differences. By continuing to implement the same 3.4 percent adjustment for coding differences in 2011 and 2012, CMS likely underestimated the impact of coding differences in 2011 and 2012, resulting in excess payments to MA plans.
GAOs findings underscore the importance of both CMS continuing to adjust risk scores to account for coding differences and ensuring that those adjustments are as complete and accurate as possible.
In its comments, CMS stated that it found our findings informative. CMS did not comment on our recommendation.
Why GAO Did This Study
The Centers for Medicare & Medicaid Services (CMS) pays plans in Medicare Advantage (MA)the private plan alternative to Medicare fee-for-service (FFS)a predetermined amount per beneficiary adjusted for health status. To make this adjustment, CMS calculates a risk score, a relative measure of expected health care costs, for each beneficiary. Risk scores should be the same among all beneficiaries with the same health conditions and demographic characteristics. Policymakers raised concerns that differences in diagnostic coding between MA plans and Medicare FFS could lead to inappropriately high MA risk scores and payments to MA plans. CMS began adjusting for coding differences in 2010. GAO (1) estimated the impact of any coding differences on MA risk scores and payments to plans in 2010 and (2) evaluated CMSs methodology for estimating the impact of these differences in 2010, 2011, and 2012. To do this, GAO compared risk score growth for MA beneficiaries with an estimate of what risk score growth would have been for those beneficiaries if they were in Medicare FFS, and evaluated CMSs methodology by assessing the data, study populations, study design, and beneficiary characteristics analyzed.
GAO recommends that CMS should improve the accuracy of its MA risk score adjustments by taking steps such as incorporating adjustments for additional beneficiary characteristics, using the most current data available, accounting for all relevant years of coding differences, and incorporating the effect of coding difference trends.
Recommendations for Executive Action
|Centers for Medicare and Medicaid Services||
Priority Rec.1. To help ensure appropriate payments to MA plans, the Administrator of CMS should take steps to improve the accuracy of the adjustment made for differences in diagnostic coding practices between MA and Medicare FFS. Such steps could include, for example, accounting for additional beneficiary characteristics, including the most current data available, identifying and accounting for all years of coding differences that could affect the payment year for which an adjustment is made, and incorporating the trend of the impact of coding differences on risk scores.